Cargando…
Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection
BACKGROUND: Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the p...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283989/ https://www.ncbi.nlm.nih.gov/pubmed/34266388 http://dx.doi.org/10.1186/s12885-021-08565-2 |
_version_ | 1783723308197347328 |
---|---|
author | Ren, Liying Chen, Dongbo Xu, Wentao Xu, Tingfeng Wei, Rongyu Suo, Liya Huang, Yingze Chen, Hongsong Liao, Weijia |
author_facet | Ren, Liying Chen, Dongbo Xu, Wentao Xu, Tingfeng Wei, Rongyu Suo, Liya Huang, Yingze Chen, Hongsong Liao, Weijia |
author_sort | Ren, Liying |
collection | PubMed |
description | BACKGROUND: Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. METHODS: The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). RESULTS: The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. CONCLUSIONS: GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08565-2. |
format | Online Article Text |
id | pubmed-8283989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82839892021-07-19 Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection Ren, Liying Chen, Dongbo Xu, Wentao Xu, Tingfeng Wei, Rongyu Suo, Liya Huang, Yingze Chen, Hongsong Liao, Weijia BMC Cancer Research BACKGROUND: Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. METHODS: The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). RESULTS: The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. CONCLUSIONS: GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08565-2. BioMed Central 2021-07-15 /pmc/articles/PMC8283989/ /pubmed/34266388 http://dx.doi.org/10.1186/s12885-021-08565-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ren, Liying Chen, Dongbo Xu, Wentao Xu, Tingfeng Wei, Rongyu Suo, Liya Huang, Yingze Chen, Hongsong Liao, Weijia Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection |
title | Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection |
title_full | Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection |
title_fullStr | Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection |
title_full_unstemmed | Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection |
title_short | Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection |
title_sort | predictive potential of nomogram based on gmwg for patients with hepatocellular carcinoma after radical resection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283989/ https://www.ncbi.nlm.nih.gov/pubmed/34266388 http://dx.doi.org/10.1186/s12885-021-08565-2 |
work_keys_str_mv | AT renliying predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT chendongbo predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT xuwentao predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT xutingfeng predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT weirongyu predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT suoliya predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT huangyingze predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT chenhongsong predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection AT liaoweijia predictivepotentialofnomogrambasedongmwgforpatientswithhepatocellularcarcinomaafterradicalresection |